Explained: Knowledge Graph in ML

• Unification of disparate data sources. Knowledge graphs can be constructed using various data sources, including semi-structured, unstructured, and structured data from relational databases. Unstructured data include free text, photos, and documents, whereas semi-structured data include Hypertext Markup Language, JavaScript Object Notation, and Extensible Markup Language. Common sources of knowledge graphs include Wikipedia and domain-specific project repositories. When knowledge graphs are blended with generative AI methods such as NLP, organizations can gather valuable insights from different data sources to create a cohesive knowledge representation.
• Knowledge extraction. Once the data is gathered, the information extraction process begins. To accomplish this, essential details from the incoming data—such as entities, relationships, and attributes—must be extracted. Techniques such as text mining, machine learning, and NLP are commonly used for this purpose.
• Graph representation. Next, the extracted knowledge is displayed in a graph format. A knowledge graph’s edges show the connections between the nodes, which represent entities or concepts. Attributes can also be connected to nodes and edges to provide more information.
• Schema and ontology. A schema or an ontology is frequently used in knowledge graphs to specify the graph’s structure and semantics. Usually based on a taxonomy, an ontology formally represents the items and their relationships. It aids in encoding the data’s meaning for programmatic usage.
• Reasonings and inference. Knowledge graphs can use reasoning techniques to draw conclusions based on the information already available or to generate new knowledge. Reasoning fills in knowledge gaps and facilitates deeper analysis and decision-making by highlighting connections that might be overlooked.
• Integration and exploration. Knowledge graphs facilitate the assimilation of fresh data sets and formats by connecting them to preexisting nodes and relationships. This makes it easier for users to explore the graph and lets them move easily between sections by clicking on related links. Because of the built-in graph structure, the information can be efficiently retrieved and explored.

Are knowledge graphs a part of machine learning?

Knowledge graphs are frequently used in tandem with machine learning techniques. Machine learning is a subfield of artificial intelligence and computer science that uses data and algorithms to mimic how humans learn. It entails creating algorithms that can learn from data and improve their accuracy over time without having to be explicitly coded. Machine learning algorithms search for patterns in massive volumes of data and use those patterns to generate predictions or conduct actions.

While knowledge graphs aren’t fundamentally a part of machine learning, they can significantly improve the capabilities and performance of machine learning models.

Both machine learning and knowledge graphs complement each other. Knowledge graphs provide organized knowledge and relationships that can improve the performance of machine learning models by reducing the need for huge, labeled data sets, facilitating transfer learning, and improving the predictability and trustworthiness of the models’ predictions.

The significance of combining knowledge graphs with AI

Knowledge graphs and AI go hand in hand in intelligent systems and information processing. Incorporating AI with knowledge graphs provides the following benefits:

• Improved context and understanding. Knowledge graphs offer an organized representation of data by capturing relationships and semantics. Large language models (LLMs) and knowledge graphs can be combined to improve the context and comprehension of AI systems. The structured representation of knowledge graphs enhances the semantic depth, making AI systems more accurate, understandable, and context-aware.
• Works in tandem with existing tools. Knowledge graphs that offer virtualization maintain data accuracy and improve productivity while easily integrating with existing tools and frameworks, such as Python and R. The semantic layer of a knowledge graph encourages reuse and interoperability, eliminating the need to start from scratch each time.
• High productivity for data workers. Data scientists and machine learning engineers often spend significant time on data-wrangling techniques that involve manual data gathering and cleansing. Since knowledge graphs enable AI models to be trained directly on unified data with uniform terminologies and synthesized sources, they can save data workers a substantial amount of time.
• Improved natural language understanding. Although knowledge graphs are excellent at capturing organized data, they can have trouble comprehending unstructured text and natural language. LLMs, which excel at comprehending natural language, can be integrated with knowledge graphs to close this gap and improve the ability of AI systems to grasp and analyze unstructured text.
• Offers enhanced decision-making. Knowledge graphs organize data relationships logically. When combined with AI, this increases the intelligence of the data, providing AI systems with the background necessary to make trustworthy decisions. This integration empowers AI systems to use structured information in knowledge graphs for improved predictions, recommendations, and insights.
• Advanced applications. AI and knowledge graphs can create new opportunities for complex applications. For instance, chatbots can use knowledge graphs to deliver context-aware responses and have deeper dialogues. Knowledge graphs include structured information that AI systems can use for various functions, including information retrieval, recommendation systems, and question-answering.